Staff AI Data Engineer at Park Place Technologies – Highland Heights | Jobyssey

Department: AI Data Engineering

Reports To: Data Engineering Team Lead / Engineering Manager

Position Overview:

Mid‑level engineer who designs and maintains scalable data pipelines, ETL processes and data platforms to support AI/ML workloads, integrating vector stores and ensuring data quality and compliance.

Key Responsibilities:

Implement and maintain scalable batch and streaming data pipelines to ingest, transform and serve data for AI/ML workloads; work with senior engineers and architects on designing pipelines and processes.
Develop ETL/ELT processes using Python and SQL to prepare training, test, and production datasets and feature stores. Experience with big data technologies (Spark, Hadoop) and flow tools (Kafka, NiFi) is a plus but not required.
Build and maintain data warehouses and lakes; integrate with vector stores to support retrieval‑augmented generation (RAG) systems. aPartner with more senior engineers to collaborate with AI Data Engineering, IT Data Engineering, Infrastructure, AI Engineering, Security and Business Leaders to deliver features for model training and inference
Implement data validation and quality checks with validation from more senior engineers; maintain documentation of data flows and schemas.
Work with more senior engineers to ensure pipelines meet data quality, observability, security and regulatory compliance standards.
Work with Model Context Protocol (MCP) to integrate into data pipelines and make modifications to existing MCP connections with guidance from more senior engineers.

Qualifications:

At least two (2) years’ experience working in a Data Engineering, Data Science, Software Development or other relevant role.
Professional experience with programming in either Python or an object-oriented programming language.
Strong knowledge of relational and NoSQL-based databases, with significant proficiency in SQL.
Understanding of ETL processes and data modeling concepts.
Exposure to data processing frameworks and tools (examples are but not all required as Apache Spark, Kafka, dlt, dbt), and cloud data services (AWS Glue, Azure Data Factory, GCP Dataflow). Experience with one or more of these tools or services is a plus but not required.
Knowledge of data warehousing, lakehouse architectures, and data modeling concepts. Experience with ML tools such as pytorch is a plus.
Understanding of machine‑learning workflows and ability to build feature stores for AI models.
Exposure to containerization (Docker), Kubernetes and continuous integration/continuous deployment (CI/CD). Experience is a plus but not required.
General understanding of AI/ML concepts with the ability and willingness to learn more.
Ability to collaborate with team leadership and Data Engineering, Infrastructure, AI Engineering, Security and Business peers.
Strong problem‑solving, communication and teamwork skills.

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